research-article

Driver drowsiness in automated and manual driving: insights from a test track study

Authors:

Thomas Kundinger,

Andreas Riener,

Nikoletta Sofra,

Klemens WeiglAuthors Info & Claims

IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces

Pages 369 - 379

https://doi.org/10.1145/3377325.3377506

Published: 17 March 2020 Publication History

Abstract

Driver drowsiness is a major cause of traffic accidents. Automated driving might counteract this problem, but in the lower automation levels, the driver is still responsible as a fallback. The impact of driver drowsiness on automated driving under realistic conditions is, however, currently unknown. This work contributes to risk and hazard assessment with a field study comparing manual to level-2 automated driving. The experiment was conducted on a test track using an instrumented vehicle. Results (N=30) show that in automated driving, driver drowsiness (self-rated with the Karolinska Sleepiness Scale) is significantly higher as compared to manual driving. Age group (20--25, 65--70 years) and driving time have a strong impact on the self-ratings. Additionally, to subjective measures, a correlation was also identified between drowsiness and heart rate data. The gained knowledge can be useful for the development of drowsiness detection systems and dynamic configuration of driver-vehicle interfaces based on user state.

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Cited By

Figalová NBieg HSchulz MPichen JBaumann MChuang LPollatos O(2023)Fatigue and mental underload further pronounced in L3 conditionally automated driving: Results from an EEG experiment on a test trackCompanion Proceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581754.3584133(64-67)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581754.3584133
von Sawitzky TGrauschopf TRiener A(2022)Hazard Notifications for Cyclists: Comparison of Awareness Message Modalities in a Mixed Reality StudyProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511127(310-322)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511127
Arefnezhad SHamet JEichberger AFrühwirth MIschebeck AKoglbauer IMoser MYousefi A(2022)Driver drowsiness estimation using EEG signals with a dynamical encoder–decoder modeling frameworkScientific Reports10.1038/s41598-022-05810-x12:1Online publication date: 16-Feb-2022
https://doi.org/10.1038/s41598-022-05810-x
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Index Terms

Driver drowsiness in automated and manual driving: insights from a test track study
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
2. Social and professional topics
  1. User characteristics

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cover image ACM Conferences

IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces

March 2020

607 pages

ISBN:9781450371186

DOI:10.1145/3377325

General Chairs:
Fabio Paternò,
Nuria Oliver,
Program Chairs:
Cristina Conati,
Lucio Davide Spano,
Nava Tintarev

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 17 March 2020

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IUI '20: 25th International Conference on Intelligent User Interfaces

March 17 - 20, 2020

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Figalová NBieg HSchulz MPichen JBaumann MChuang LPollatos O(2023)Fatigue and mental underload further pronounced in L3 conditionally automated driving: Results from an EEG experiment on a test trackCompanion Proceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581754.3584133(64-67)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581754.3584133
von Sawitzky TGrauschopf TRiener A(2022)Hazard Notifications for Cyclists: Comparison of Awareness Message Modalities in a Mixed Reality StudyProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511127(310-322)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511127
Arefnezhad SHamet JEichberger AFrühwirth MIschebeck AKoglbauer IMoser MYousefi A(2022)Driver drowsiness estimation using EEG signals with a dynamical encoder–decoder modeling frameworkScientific Reports10.1038/s41598-022-05810-x12:1Online publication date: 16-Feb-2022
https://doi.org/10.1038/s41598-022-05810-x
Chong SBaldwin C(2021)The Origins of Passive, Active, and Sleep-Related FatigueFrontiers in Neuroergonomics10.3389/fnrgo.2021.7653222Online publication date: 23-Dec-2021
https://doi.org/10.3389/fnrgo.2021.765322
Orii LTosca DKun AShaer O(2021)Perceptions of Trucking Automation: Insights from the r/Truckers Community13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3409118.3475154(137-146)Online publication date: 9-Sep-2021
https://dl.acm.org/doi/10.1145/3409118.3475154
Kundinger TRiener ABhat R(2021)Performance and Acceptance Evaluation of a Driver Drowsiness Detection System based on Smart Wearables13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3409118.3475141(49-58)Online publication date: 9-Sep-2021
https://dl.acm.org/doi/10.1145/3409118.3475141
Pichen JStoll TBaumann M(2021)From SAE-Levels to Cooperative Task Distribution:An Efficient and Usable Way to Deal with System Limitations?13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications10.1145/3409118.3475127(109-115)Online publication date: 9-Sep-2021
https://dl.acm.org/doi/10.1145/3409118.3475127
Suhir E(2021)Driver propensity to fatigue and drowsiness: a probabilistic approachTheoretical Issues in Ergonomics Science10.1080/1463922X.2021.188971023:1(104-120)Online publication date: 1-Apr-2021
https://doi.org/10.1080/1463922X.2021.1889710
Kaindl HPaul G(2020)A Probabilistic Model of Taking-Over Control from Semi-autonomous VehiclesHuman Systems Engineering and Design III10.1007/978-3-030-58282-1_52(332-337)Online publication date: 30-Aug-2020
https://doi.org/10.1007/978-3-030-58282-1_52

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